UniArt: 一种统一的3D表示方法，用于从单张图像生成具有开集关节的3D可动物体
UniArt: Unified 3D Representation for Generating 3D Articulated Objects with Open-Set Articulation
AI总结：
UniArt通过统一的潜在表示和开集关节预测方法，实现了从单张图像生成高质量可动3D物体的端到端合成。
摘要：
可动的3D物体在现实模拟和具身机器人中起着至关重要的作用，但手动构建此类资产仍然成本高昂且难以扩展。在本文中，我们提出了UniArt，一种基于扩散的框架，能够端到端地从单张图像直接合成完整的可动3D物体。与以往多阶段技术不同，UniArt建立了一个统一的潜在表示，共同编码几何、纹理、部分分割和运动学参数。我们引入了一种可逆的关节到体素嵌入，将关节特征在空间上对齐到体素几何，使模型能够同时学习连贯的运动行为和结构形成。此外，我们将关节类型预测作为开集问题进行建模，消除了对固定关节语义的需求，允许泛化到新的关节类别和未见的物体类型。在PartNet-Mobility基准测试中，实验表明UniArt在网格质量和可动准确性方面达到了最先进的水平。
英文摘要：
Articulated 3D objects play a vital role in realistic simulation and embodied robotics, yet manually constructing such assets remains costly and difficult to scale. In this paper, we present UniArt, a diffusion-based framework that directly synthesizes fully articulated 3D objects from a single image in an end-to-end manner. Unlike prior multi-stage techniques, UniArt establishes a unified latent representation that jointly encodes geometry, texture, part segmentation, and kinematic parameters. We introduce a reversible joint-to-voxel embedding, which spatially aligns articulation features with volumetric geometry, enabling the model to learn coherent motion behaviors alongside structural formation. Furthermore, we formulate articulation type prediction as an open-set problem, removing the need for fixed joint semantics and allowing generalization to novel joint categories and unseen object types. Experiments on the PartNet-Mobility benchmark demonstrate that UniArt achieves state-of-the-art mesh quality and articulation accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
